{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 100)\n",
    "pd.set_option('precision', 4)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 6)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 468 entries, 0 to 467\n",
      "Data columns (total 5 columns):\n",
      "iso2c        468 non-null object\n",
      "Country      468 non-null object\n",
      "Year         468 non-null int64\n",
      "PerCapGDP    458 non-null float64\n",
      "GDP          458 non-null float64\n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 18.4+ KB\n"
     ]
    }
   ],
   "source": [
    "gdp= pd.read_csv(\"/data/gdp.csv\")\n",
    "gdp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso2c</th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>PerCapGDP</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1960</td>\n",
       "      <td>2294.5688</td>\n",
       "      <td>4.1093e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1961</td>\n",
       "      <td>2231.2938</td>\n",
       "      <td>4.0768e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1962</td>\n",
       "      <td>2255.2300</td>\n",
       "      <td>4.1979e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1963</td>\n",
       "      <td>2354.8391</td>\n",
       "      <td>4.4657e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1964</td>\n",
       "      <td>2529.5182</td>\n",
       "      <td>4.8883e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso2c Country  Year  PerCapGDP         GDP\n",
       "0    CA  Canada  1960  2294.5688  4.1093e+10\n",
       "1    CA  Canada  1961  2231.2938  4.0768e+10\n",
       "2    CA  Canada  1962  2255.2300  4.1979e+10\n",
       "3    CA  Canada  1963  2354.8391  4.4657e+10\n",
       "4    CA  Canada  1964  2529.5182  4.8883e+10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso2c</th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>PerCapGDP</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1960</td>\n",
       "      <td>2294.5688</td>\n",
       "      <td>4.1093e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1961</td>\n",
       "      <td>2231.2938</td>\n",
       "      <td>4.0768e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1962</td>\n",
       "      <td>2255.2300</td>\n",
       "      <td>4.1979e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-01</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1963</td>\n",
       "      <td>2354.8391</td>\n",
       "      <td>4.4657e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>CA</td>\n",
       "      <td>Canada</td>\n",
       "      <td>1964</td>\n",
       "      <td>2529.5182</td>\n",
       "      <td>4.8883e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           iso2c Country  Year  PerCapGDP         GDP\n",
       "Year                                                 \n",
       "1960-01-01    CA  Canada  1960  2294.5688  4.1093e+10\n",
       "1961-01-01    CA  Canada  1961  2231.2938  4.0768e+10\n",
       "1962-01-01    CA  Canada  1962  2255.2300  4.1979e+10\n",
       "1963-01-01    CA  Canada  1963  2354.8391  4.4657e+10\n",
       "1964-01-01    CA  Canada  1964  2529.5182  4.8883e+10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp = gdp.set_index(pd.to_datetime(gdp.Year, format=\"%Y\"))\n",
    "gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CA', 'CN', 'DE', 'GB', 'IL', 'IN', 'JP', 'SG', 'US'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.iso2c.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0ce0e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 8))\n",
    "gdp.groupby([\"Country\"]).GDP.plot(legend = True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1960-01-01    3007.1234\n",
       "1961-01-01    3066.5629\n",
       "1962-01-01    3243.8431\n",
       "1963-01-01    3374.5152\n",
       "1964-01-01    3573.9412\n",
       "Name: PerCapGDP, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa = gdp.query(\"iso2c == 'US'\").PerCapGDP\n",
    "usa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18993780>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a184680f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a18ab5438>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a189936a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_rolling_mean = usa.rolling(window = 10, center = False).mean() \n",
    "usa_rolling_std = usa.rolling(window = 10, center = False).std() \n",
    "\n",
    "plt.plot(usa, label = \"actual\")\n",
    "plt.plot(usa_rolling_mean, label = \"moving avg\")\n",
    "plt.plot(usa_rolling_std, label = \"Std\")\n",
    "\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a18b879b0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18461eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_log = np.log(usa)\n",
    "usa_rolling_mean = usa_log.rolling(window = 10, center = False).mean() \n",
    "usa_rolling_std = usa_log.rolling(window = 10, center = False).std() \n",
    "\n",
    "plt.plot(usa_log)\n",
    "plt.plot(usa_rolling_mean)\n",
    "plt.plot(usa_rolling_std)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller, acf, pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistic                  1.9127\n",
      "p-value                         0.9985\n",
      "#Lags Used                      2.0000\n",
      "Number of Observations Used    49.0000\n",
      "Critical Value (1%)            -3.5715\n",
      "Critical Value (5%)            -2.9226\n",
      "Critical Value (10%)           -2.5993\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def test_stationary(tseries):\n",
    "    dftest = adfuller(tseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], \n",
    "            index=['Test Statistic','p-value'\n",
    "                   ,'#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)\n",
    "\n",
    "test_stationary(usa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the p-value is larger than 0.05, the moving average is not constant over time and the null hypothesis of the Dickey-Fuller test cannot be rejected. This shows that the weekly time series is not stationary. Before you can apply ARIMA models for forecasting, you need to transform this time series into a stationary time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistic                 -2.9833\n",
      "p-value                         0.0365\n",
      "#Lags Used                      1.0000\n",
      "Number of Observations Used    50.0000\n",
      "Critical Value (1%)            -3.5685\n",
      "Critical Value (5%)            -2.9214\n",
      "Critical Value (10%)           -2.5987\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationary(usa_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the p-value is now smaller than 0.05, we have necessary evidence to reject the null hypothesis of the Dickey-Fuller test. This shows that the time series is stationary at log scale. We can apply ARIMA model forcasting on this log scaled data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove trend and seasonality with decomposition¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZlBdkkGvT8RjTJwtQxrhUnaBTU+8OOqgPUHugHX8wTFuX80xQ9zNC7YfkDQIne2r5uEwWTBrHFxZN4rgJOcwqzaUsP8Oe/zHmKFiAMmNG9wCEfa3OwIPudN37WoLsdNM0xN4DykzzMLkgk9yMVEpyfGQWepwh2O6r2A1IziW6TBsVZ0ycWYAyo0ZHV4Tdze184F5q6778VnvAmfng0OeAANI8KRTnpFNRlMlnFpYxrTjbeZU4Mx/YCDhjEscClBkRQpEojQFn6HV9ayd1rcEjBqA0Twpl4zIoH5fB7NnjmZiXwfi8D1N1j8/xkZ9pD58ak6wsQJmEUFU6QhFaO8I0tXXSFOiiMdDpvrp6lvWtQRr8nexv7+LQAaeHBqDuaXicVybF2Tb5qDEjmQUoc9SCoQgtHSGa20M0t3dxoD2EPxjCH3QmGvUHQwQ6w7QGwwSCYVo6QrR2hGgNhmjpCBGK9P6IQ5o3heLs9J5BBydOGdcz5Lokx+dMSJrroyTHApAxo5kFqFFMVekMRwmGInSFo3RFooQiSigS7XnfGYrSEXJGpbV3RejoitARctbbYoJMd9AJBJ315o6uw/L5HCoj1UO2z0uOz0tOupfcjFTKxmWQl5FKXkYquT5nOS4zlaKcdIrcoJSd7rXLbsYYC1BDTVUJRZQuNyh0ht1gEY72BI9gyFl2hCLO+3CUUDhKKBIlHFW6wlHCUSe4dLnbtHd9WL87sMTuo8Pd77HITncmDs32Ocscn5cJuT5yfF7yM53JQ/MzUxmXmUZ+Riq5buDJ8Tmj3CzBnDHmWCRNgOor2+5Q2FzXyhNvvk9UlaiCqhNIut9Ho05ACUe0JzB0LyNRJRxVou4yEo32lIXCUUJR5wzFCTBKKBo97N7J0fCmCF6PkOpJISPVQ2aaB1+qh4w0Z31cZirpqR4y3TJfqvt5qgdfagpp3hRSPSmkeZxlqkdI8zrlmWleMtM8PfvNTPPiS02xsxhjTEIlRYASEQ9wLzHZdkXkaVXdNBTft7c1yDPr6xDnu0kREIEUEQRISRHSPCl4PYI3xfnH3OtJwZsiZKR6SEkRvCmCJ0XwiODxOO9TPd1BwKnfHQjS3UCQ5kkhPdVDmsd5n+5N6QkkvlR33eusp3s9PQHJmyJ2r8UYM+YkRYDCybh7WLZdnAln4+7M40p461/PGopdG2OMiZNkCVBl9J5t9yCxGXWBgIi8N0TtKcLJUzXWWT9YH3SzfnBYP8SnD6YMpFKyBKgBZdWNzag7pI0RWTuQmXZHO+sH64Nu1g8O64fh7YNkGWbVXxZeY4wxY1CyBKg1uNl2RSQNJ9vu0wlukzHGmARKikt8qhruJ9tuIgz5ZcQRwvrB+qCb9YPD+mEY+2DEZtQ1xhgzuiXLJT5jjDHmIBagjDHGJKUxEaBEZIWI1IvIhpiy+SLyuoi8IyJ/FpHcmM/muZ9tdD/3ueV3iMgHIhJIxHEcq8H0g4hcKiLrYl5REVngfjaW+iFVRB5xyzeLyA397WekGGQfpInIQ2752yKyJGabkf5bmCQiL7n/bTeKyLVueYGIPC8iW93lOLdcROQeEakRkfUicmLMvv4mIs0i8kyijudoHEUfHO/+TjpF5LpD9hXfvwlVHfUv4HTgRGBDTNka4Ax3/QrgdnfdC6wH5rvvCwGPu34yUAoEEn1MQ90Ph2w3F9ge837M9ANwCbDSXc8EdgIVfe1npLwG2QdXAw+56yVANZAySn4LpcCJ7noOsAWYDfwUuN4tvx74d3f9POCvOM9ungysjtnXUuAC4JlEH9cQ90EJcBJwB3DdkX5Xx/IaE2dQqvoKsP+Q4uOAV9z154HPuutnA+tV9W132yZVjbjrb6hq3TA0eUgMsh9ifQl4ImY/Y6kfFMgSES+QAXQBrf3sZ0QYZB/MBl50t6sHmoEq9/1I/y3Uqepb7rof2Iwzs80y4BG32iPAhe76MuBRdbwB5ItIqbv9i4B/ONsfD4PtA1WtV9U1QKiXfcX1b2JMBKg+bAA+7a5/ng8fFJ4JqIg8JyJvicgPEtK64dNXP8S6mJgANUr11Q+/B9qAOuB94G5VHZFBaQD66oO3gWUi4hWRSmARvf9ORjQRqQAWAquB8d2B112WuNV6m5atbPhaObQG2AfDZiwHqCuAq0WkGue0tsst9wKnAZe6y8+IyNLENHFY9NUPAIjIYqBdVUfcfZZB6qsfPgJEgIlAJfA9EZmamCYOub76YAXOP8RrgV8ArwHhhLRwiIhINvAU8F1Vbe2vai9lo+JZnUH0wbBJigd1E0FV38W5nIeIzAQ+5X5UC/xDVRvdz57Fuab6YiLaOdT66YduX2T0nz311w+XAH9T1RBQLyL/g3N5a3tCGjqE+uoDVQ0D/7u7noi8BmxNRBuHgoik4vzD/Jiq/sEt3icipapa517Cq3fLR+W0bIPsg2EzZs+gRKTEXaYANwP3uR89B8wTkUz3vsMZDFHaj2TQTz90l30eWJmY1g2ffvrhfeDj7uitLJwb4+8mppVDq68+cP8Wstz1s4CwDlGutuEmIgI8CGxW1Z/FfPQ0cLm7fjmwKqb8K+7v4WSgZSTfg4Oj6oPhk+gRJMPxwjkDqMO5qVcLXAlcizNaZQtwJ+6sGm79LwMbca7J/zSm/Kfu9lF3eWuij22I+2EJ8EYv+xkz/QBkA79zfw+bgO/3t59EH9sQ9UEF8B7OzfMXgCmj6LdwGs4luvXAOvd1Hs7o3RdxzhRfBArc+oKTXHUb8A5QFbOv/wYagA63L85J9PENUR9McI+vFWfATC2Q29fv6ljaZlMdGWOMSUpj9hKfMcaY5GYByhhjTFKyAGWMMSYpWYAyxhiTlCxAGWOMSUoWoIwxxiQlC1DGGGOSkgUoY4wxSckClDHGmKRkAcoYY0xSsgBljDEmKVmAMsYYk5QsQBljjElKAwpQIrJTRN4RkXUistYtKxCR50Vkq7sc55aLiNwjIjUisl5ETozZz+Vu/a0icnlM+SJ3/zXutr1lrTTGGDOGDCjdhojsxMl70hhT9lNgv6reKSLXA+NU9Ycich5wDU4+kcXAL1V1sYgU4KSMrsLJPVINLFLVAyLyJk4umjeAZ4F7VPWv/bWpqKhIKyoqBn3AxhhjEqu6urpRVYuPVO9YUr4vw0loB/AI8DLwQ7f8UXUi3xsiku+mC14CPK+q+wFE5HngXBF5GSfZ1etu+aPAhUC/AaqiooK1a9ceQ/ONMcb0pzUYoqY+QE19gG3dy4YAz3znY2SnH334EJFdA6k30G9Q4O8iosB/qOr9wHh1Ux2rk7O+xK1bBnwQs22tW9ZfeW0v5YcRkauAqwAmT548wKYbY8zo1ODvZHNdK5vrWqlrCRIMRQiGInSEInSEogS7InRGopTm+phWksW04myml2QztTi7J8BEo8ru5g62NQTY3tDG9sYA2+rbqGkI0ODv7PmuNE8KlUVZzJmYR3tn+JgC1EAN9BtOVdU9bhB6XkTe7adub/eP9CjKDy90AuP9AFVVVZYK2BgzJqgqtQc6qN51gE1uQNpc56cx8GEAyUn34kvzkJHqvJz1FHJTvWzZ5+f5zfuIRD/8Z3NCro/cDC+7mtrpDEd7ynN9XqaVZHPGzGKml2Qz3Q1q5eMy8HqGd1zdgAKUqu5xl/Ui8kfgI8A+ESl1z55KgXq3ei0wKWbzcmCPW77kkPKX3fLyXuobY8yYFI0qNQ0B3tyxnzd37GfNzv3UtQQB50xmxvhslhxXzKzSXGZNyOH40lwKstL63WdXOMr7+9uoqW9jW4Nzqa61I8QZM4uZWpzNtOJsphZnUZiVRrKMUztigBKRLCBFVf3u+tnAbcDTwOXAne5ylbvJ08C3RWQlziCJFjeIPQf8pHu0n7ufG1R1v4j4ReRkYDXwFeD/Hs3BhEIhamtrCQaDR7P5iOfz+SgvLyc1NTXRTTHGDIKqsrOpnVdrGvmfrY2s3tHEgfYQACU56ZxUWcDiygKqphQwY3w2qUdxJpPmTWF6SQ7TS3Li3fwhM5AzqPHAH92I6gUeV9W/icga4EkRuRJ4H/i8W/9ZnBF8NUA78FUANxDdDqxx693WPWAC+CbwMJCBMzii3wESfamtrSUnJ4eKioqk+T+A4aKqNDU1UVtbS2VlZaKbY4w5gqZAJ69ta+LVrY28WtPI7uYOAMryM1g6azwfcYPS5ILMMffvWbcjBihV3Q7M76W8CVjaS7kCV/exrxXAil7K1wInDKC9/QoGg2MyOAGICIWFhTQ0NCS6KcaYXrS0h3hjRxNvbG/i9W1NvLvXD0COz8sp0wr5xpJpnDa9iIrCsRuQDjX0wzCG2Vj+DzuWj92YZBKJKu/vb+e9va2s2XmA17c1sXlvK6qQ7k2hqmIc1509k1OnFzG3LG/YBx+MFKMuQBljzHBQVVo6Quxr7aT2QDtb9gXYss/Pln1+auoDPSPj0rwpLJo8ju8unclHpxUyf1Ie6V5Pgls/MliAiqOmpiaWLnWueu7duxePx0NxsfOw9JtvvklaWv+jbAajvLycDRs2kJ+fH7d9GmMgFInSFOiiwd9JQyBIo7+LhkAnDf5O6v1B9rV2sq81SL2/k66Y4dngDN2eOSGHj04tZOaEHGaOz+H4CTn4Ui0gHQ0LUHFUWFjIunXrALj11lvJzs7muuuuO6iOqqKqpKTYKb0xiRboDLNhdwvra5t5u7aFd2pbeH9/e691s9O9lOSmMz7HR9WUcYzP9VGck874XB8T831ML8khL8NG0MaTBahhUFNTw4UXXshpp53G6tWreeaZZ1i/fj233XYbnZ2dzJgxgxUrVpCVlUV5eTlf+9rXWLVqFZFIhN///vfMnDmThoYGLrnkEpqamli8eDEDmUPRGPOhlvYQm+pa2VTXysY9LayvbWFbQ4DuP6Wy/AzmT8rjohPLKM5Jpzg7nSJ3WZyTbmdBCTBqA9SP/ryRTXta47rP2RNzueWCOUe17aZNm3jooYe47777qK+v58477+TFF18kMzOTO+64g1/+8pfceOONAIwfP55//vOf3HPPPfzsZz/jvvvu45ZbbuHMM8/kxhtvZNWqVdx3333xPDRjRo3OcIRdTe1sbwjw7l4/m/Y4Qan2QEdPneKcdOaV5XHBvInMm5THvLI8CrPTE9hq05tRG6CSzbRp0zjppJMAeO2119i0aROnnHIKAF1dXZx22mk9dS+66CIAFi1axLPPPgvAK6+80rO+bNkycnJGzsN2xgyF1mCIrfv8bN0XcGdGaGN7Q4D397fTPaOPCEwtymLh5HFcungKcybmMqs0l+IcC0YjwagNUEd7pjNUsrKyetZVlXPPPZff/va3vdZNT3f+eDweD+FwuKfchpGbsehAWxc7mtqo6R4lVx9gy14/e1s/nDEm3etOZFqWx6fnT2RayYdT92Smjdp/5kY9+y+XAKeccgrXXnst27dvZ+rUqbS1tbFnzx5mzJjR5zann346jz32GNdffz1//vOf8fv9w9hiY4ZOOBJln7+TuuYOdjd3sKOxjZ2NbexoamdnYxstHaGeuuneFKaXZHPKtEJmjM9h5vhsZo7PoSw/g5QU+x+40WYgc/FNAh4FJgBR4H5V/aWI3Ap8HeieuuBGVX3W3eYG4EogAnxHVZ9zy88Ffgl4gN+o6p1ueSWwEigA3gIuU9WueB1kshk/fjwPPvggF198MV1dzmH+5Cc/6TdA/ehHP+JLX/oSTz75JGeeeSZlZb1mJDEm6agq9f5Otje0OcGnqY3dBzrY09JBXXOQen+QmEm2EYGJeRlUFmVxwfxSKgqzqCxyUkVMKsjEY4FozDhiRl13pvJSVX1LRHJwMuFeCHwBCKjq3YfUnw08gTPj+UTgBWCm+/EW4CycGczXAF9S1U0i8iTwB1VdKSL3AW+r6q/7a1dVVZUemrBw8+bNzJo1awCHPXpZH5jhFo0qjYFO6lqC1LUE2dvSQV1rkNoDHexocAJSe1ekp36aN4Xy/AxK832U5mUwMc9HaX4GE/J8lOVnMLkg00bMjXIiUq2qVUeqN5C5+OqA7sSEfhHZTB8JBV3LgJWq2gnsEJEanGAFUOPO7Yc72/kyd38fBy5x6zwC3Ar0G6CMMUMvGlXausI0BrrYfaCD2gMpzFbEAAAgAElEQVTt7G7ucNedS3L7WoOEowf/j26aJ4XSfB+VRVksnlrA1KIsKouyqSzOojTXZ5fjzIAM6h6UiFQAC3HSYpyKk1bjK8Ba4HuqegAneL0Rs1lshtxDM+ouBgqBZlUN91L/0O+3jLrGHANVpTUYpq6lgz3NHexuDlLX7KzvbQ0S6AzT1hlxl+GDzny6pQiU5mVQNi6Dj1QWMDHfx4S8DEpzfUzIc14FmWkWhMwxG3CAEpFs4Cngu6raKiK/Bm7HyX57O/B/gCvoO0Nub1MnxD2jrqqO2dFu9vCu6dadxrumIUDNvgA19QFnvT5w0KADAG+KOIEl10dJjo+sIi/Z6R6y0rxkpXvJTvdSkJVG2bgMysdlMCHXZ5ObmmExoAAlIqk4wekxVf0DgKrui/n8AeAZ921fGXXpo7wRyBcRr3sWddQZdX0+H01NTRQWFo65INWdD8rn8yW6KWYYdXRF2NH4YYbU7Q1tPcuO0IdnP4VZaUwryeZT80qpKMxkYn4GE/MzKMvPoCg73QYemKQ0kFF8AjwIbFbVn8WUl7r3pwA+A2xw158GHheRn+EMkpgBvIlzpjTDHbG3G/gicImqqoi8BHwOZyRfbHbeQSkvL6e2tnbM5kTqzqhrRoeucJTGgDMx6d6WIHu6ByC4gxHqmp3BCBrzUGpZfgbTirNZXFnI9JLsnteR0oEbk4wGcgZ1KnAZ8I6IrHPLbgS+JCILcC7H7QT+F4CqbnRH5W0CwsDVqhoBEJFvA8/hDDNfoaob3f39EFgpIj8G/okTEActNTXVssmaESEYilB7oIMPDrRTu7+d2gMd1Ps/nDG7wd/Zk/I7Vro3hYn5zmW2k6cWMqUwi2klzhDsyqIsG/1mRpUjDjNPVr0NMzcmmagqDf5O3t3r5729ft7d62dHY4APDnTQ4O88qG6aJ4WS3PSeSUpLctMpzva5y3RK831MzMsgPzN1zF2+NqNP3IaZG2P6F+gM84F7FlR7oJ1dTe28t9fPe/v87G/78Hnzkpx0ppdk8/HjSigfl8GkgsyeZXF2uo16M+YQFqCMOYSqsrc1yLb6Nj440E4gGO4Zdt3W5QzDbusMs8/vPIzafMiluMw0DzPG53D27PEcNyGH4ybkcPyEXLsPZMwgWYAyY1IwFKG+tZN9fmcAwq6mNra5I+C21Qdo6+X5n8w0T8+w66x0D0XZ6SyYlE/5uEwmjXPOhsrHZVCQlWaX4YyJAwtQZtSJRJV69+ym9kA7tfudWQ/2tDizHuxr7TzsWSBwRsBNLc7i81WT3Nmws5hSmEWuz0tmmteGYhszzCxAmRGnrwBU2+zcB9rT3EEocvDgn5KcdCbmOxOQnjy1kPG5PkrcdN0luelMLsi0tAzGJBn7izRJQVVp7QjTEOikKdBJY6CLxoAz5Lq+tZOGQCf1rZ3U+zvZ39bJIVO/UZyTTvm4DOaV5/OpuaWUx1xym5ifYcOvjRmBLECZuAlFogSCYfzBMP7OEP5g2HnfGSIQDNPSEaKlI0RrR8x6MMT+ti6aAl10RaKH7dOTIhRlp1Gck86EPB/zyvMods98ukfBlVkAMmZUsgBlUFU6w1HauyK0d4UJhiK0dzkThnYHmUBnuOe9PxiiuSNES3uI5o4umtuddX9n+IjflZHqITfDS15GKrm+VCbk+jh+Qi5FOWkUZ6dT5L4Ks9Moyk6nICvN7v0YM0ZZgEoS0agTJIKhCMFwhFBY6YpECUejhMJKKBolFI4SiigdoQ8DSUdXhPZQhGBXhI6Q8wqGos6yq/t9hFBE6QpHCUWidEWcZSiidLrbHHrJrC+pHiHXl0peZir5GamU5PiYOT6H/Iw0J+hkeMnxpZKd7iXX5yXbF/M+w0u61850jDEDkzQBqq9su0OhMdDJht0tqIKiRKMQVUVxziYiUdx/wKOEo0rY/cc8HHXeRyJKRJVIVAlHlWhUCUXUrR+lK6w924ciUTrDUbrCTmDoDDnLrnCUzrATTIKhCJ3hwy9vDZYvNYXMNC8ZqR7SU1PISPWQkeohM81LmjeFVI+Q6kkhzZPiLL3OKzPNQ0aah0y3bkaa56Ah1Tk+Z5ntswBjjBk+SRGgRMQD3EtMtl0ReVpVNw3F97216wBX/bb6mPeTIs49Ek+K4E35MAA4L2fd60kh3Q0E2eleCrPcwOBJId3rwZeagi/VQ3qqu+71OO+9KXg9Qpq7j9h9Z6Q6ASUjrTsAOfXt2RtjzGiSFAEKJ+PuYdl2cSacjf+XVRbwh2+dggApIqSIIOLMBi04AefDAPNh8PF6UvC6AckjYlPTGGPMEEqWAFVG79l2+1RdXd0oIruGqD1FOHmqxjrrB+uDbtYPDuuH+PTBlIFUSpYANaCsurEp34Gb3Ay78W+MyNqBzLQ72lk/WB90s35wWD8Mbx8kS4DqLwtvj9iU78YYY0a3lEQ3wLUGN9uuiKThZNt9OsFtMsYYk0BJcQalquF+su0mgp2lOawfrA+6WT84rB+GsQ9GbEZdY4wxo1uyXOIzxhhjDmIByhhjTFIaEwFKRFaISL2IbIgpmy8ir4vIOyLyZxHJjflsnvvZRvdzn1t+h4h8ICKBRBzHsRpMP4jIpSKyLuYVFZEF7mdjqR9SReQRt3yziNzQ335GikH2QZqIPOSWvy0iS2K2Gem/hUki8pL733ajiFzrlheIyPMistVdjnPLRUTuEZEaEVkvIifG7OtvItIsIs8k6niOxlH0wfHu76RTRK47ZF/x/ZtQ1VH/Ak4HTgQ2xJStAc5w168AbnfXvcB6YL77vhDwuOsnA6VAINHHNNT9cMh2c4HtMe/HTD8AlwAr3fVMYCdQ0dd+RsprkH1wNfCQu14CVAMpo+S3UAqc6K7nAFuA2cBPgevd8uuBf3fXzwP+ivPs5snA6ph9LQUuAJ5J9HENcR+UACcBdwDXHel3dSyvMXEGpaqvAPsPKT4OeMVdfx74rLt+NrBeVd92t21S1Yi7/oaq1g1Dk4fEIPsh1peAJ2L2M5b6QYEsEfECGUAX0NrPfkaEQfbBbOBFd7t6oBmoct+P9N9Cnaq+5a77gc04M9ssAx5xqz0CXOiuLwMeVccbQL6IlLrbvwj4h7P98TDYPlDVelVdA4R62Vdc/ybGRIDqwwbg0+765/nwQeGZgIrIcyLyloj8ICGtGz599UOsi4kJUKNUX/3we6ANqAPeB+5W1REZlAagrz54G1gmIl4RqQQW0fvvZEQTkQpgIbAaGN8deN1liVutt2nZyoavlUNrgH0wbMZygLoCuFpEqnFOa7vcci9wGnCpu/yMiCxNTBOHRV/9AICILAbaVXXE3WcZpL764SNABJgIVALfE5GpiWnikOurD1bg/EO8FvgF8Bpw5OyUI4iIZANPAd9V1db+qvZSNiqe1RlEHwybpHhQNxFU9V2cy3mIyEzgU+5HtcA/VLXR/exZnGuqLyainUOtn37o9kVG/9lTf/1wCfA3VQ0B9SLyPziXt7YnpKFDqK8+UNUw8L+764nIa8DWRLRxKIhIKs4/zI+p6h/c4n0iUqqqde4lvHq3fEDTso00g+yDYTNmz6BEpMRdpgA3A/e5Hz0HzBORTPe+wxkMUdqPZNBPP3SXfR5YmZjWDZ9++uF94OPu6K0snBvj7yamlUOrrz5w/xay3PWzgLAOUa624SYiAjwIbFbVn8V89DRwubt+ObAqpvwr7u/hZKBlJN+Dg6Pqg+GT6BEkw/HCOQOow7mpVwtcCVyLM1plC3An7qwabv0vAxtxrsn/NKb8p+72UXd5a6KPbYj7YQnwRi/7GTP9AGQDv3N/D5uA7/e3n0Qf2xD1QQXwHs7N8xeAKaPot3AaziW69cA693UezujdF3HOFF8ECtz6gpNcdRvwDlAVs6//BhqADrcvzkn08Q1RH0xwj68VZ8BMLZDb1+/qWNpmUx0ZY4xJSmP2Ep8xxpjkZgHKGGNMUrIAZYwxJilZgDLGGJOULEAZY4xJShagjDHGJCULUMYYY5KSBShjjDFJyQKUMcaYpGQByhhjTFKyAGWMMSYpWYAyxhiTlCxAGWOMSUpxC1Aicq6IvCciNSJyfS+fp4vIf7qfr3ZTCyMiFSLSISLr3Nd9h25rjDFm7IlLRl0R8eDkSDkLJwfIGhF5Wg9OanYlcEBVp4vIF4F/By52P9umqgvi0RZjjDGjQ7xSvn8EqFHV7QAishJYxsGZaJcBt7rrvwf+n5vJ8agUFRVpRUXF0W5ujDEmQaqrqxtVtfhI9eIVoMqAD2Le1wKL+6qjqmERacHJ2AhQKSL/xMnQeLOq/ndvXyIiVwFXAUyePJm1a9fGqfnGGGOGi4jsGki9eN2D6u1M6NBUvX3VqQMmq+pC4F+Ax0Ukt7cvUdX7VbVKVauKi48YfI0xxoxg8QpQtcCkmPflwJ6+6oiIF8gD9qtqp6o2AahqNbANmBmndhljjBmh4hWg1gAzRKRSRNKALwJPH1LnaeByd/1zwH+pqopIsTvIAhGZCswAtsepXcYYY0aouNyDcu8pfRt4DvAAK1R1o4jcBqxV1aeBB4HfikgNsB8niAGcDtwmImEgAnxDVffHo13GGNMtFApRW1tLMBhMdFPGDJ/PR3l5OampqUe1vageeqtoZKiqqlIbJGGMGagdO3aQk5NDYWEhxzCA2AyQqtLU1ITf76eysvKgz0SkWlWrjrQPm0nCGDMmBINBC07DSEQoLCw8pjNWC1DGmDHDgtPwOtb+tgBljDHD6I477mDOnDnMmzePBQsWsHr16kQ36TBLlizpec70vPPOo7m5mebmZn71q18Nazvi9aCuMcaYI3j99dd55plneOutt0hPT6exsZGurq5EN6tfzz77LAA7d+7kV7/6Fd/61reG7bvtDMoYY4ZJXV0dRUVFpKenA1BUVMTEiROprq7mjDPOYNGiRZxzzjnU1dUB8MADD3DSSScxf/58PvvZz9Le3g7A7373O0444QTmz5/P6aefDjj32L761a8yd+5cFi5cyEsvvQTAww8/zEUXXcS5557LjBkz+MEPftDTnm9+85tUVVUxZ84cbrnlll7bXFFRQWNjI9dffz3btm1jwYIFfP/73+eyyy5j1apVPfUuvfRSnn760KeLjo2dQRljxpwf/Xkjm/a0xnWfsyfmcssFc/qtc/bZZ3Pbbbcxc+ZMPvGJT3DxxRdzyimncM0117Bq1SqKi4v5z//8T2666SZWrFjBRRddxNe//nUAbr75Zh588EGuueYabrvtNp577jnKyspobm4G4N577wXgnXfe4d133+Xss89my5YtAKxbt45//vOfpKenc9xxx3HNNdcwadIk7rjjDgoKCohEIixdupT169czb968Xtt+5513smHDBtatWwfAP/7xD37+85+zbNkyWlpaeO2113jkkUfi0pfd7AzKGGOGSXZ2NtXV1dx///0UFxdz8cUX8x//8R9s2LCBs846iwULFvDjH/+Y2tpaADZs2MDHPvYx5s6dy2OPPcbGjRsBOPXUU1m+fDkPPPAAkUgEgFdffZXLLrsMgOOPP54pU6b0BKilS5eSl5eHz+dj9uzZ7NrlTIX35JNPcuKJJ7Jw4UI2btzIpk2bDm1yn8444wxqamqor6/niSee4LOf/Sxeb3zPeewMyhgz5hzpTGcoeTwelixZwpIlS5g7dy733nsvc+bM4fXXXz+s7vLly/nTn/7E/Pnzefjhh3n55ZcBuO+++1i9ejV/+ctfWLBgAevWraO/Z1q7Lyl2f384HGbHjh3cfffdrFmzhnHjxrF8+fJBDwm/7LLLeOyxx1i5ciUrVqwY1LYDkfCEhe5nN7jl74nIOfFqkzHGJJP33nuPrVu39rxft24ds2bNoqGhoSdAhUKhnjMlv99PaWkpoVCIxx57rGe7bdu2sXjxYm677TaKior44IMPOP3003vqbNmyhffff5/jjjuuz7a0traSlZVFXl4e+/bt469//Wu/bc/JycHv9x9Utnz5cn7xi18AMGdO/IN+whMWishsnGmP5gATgRdEZKaqRuLRNmOMSRaBQIBrrrmG5uZmvF4v06dP5/777+eqq67iO9/5Di0tLYTDYb773e8yZ84cbr/9dhYvXsyUKVOYO3duT4D4/ve/z9atW1FVli5dyvz58zn++OP5xje+wdy5c/F6vTz88MMHnTkdav78+SxcuJA5c+YwdepUTj311H7bXlhYyKmnnsoJJ5zAJz/5Se666y7Gjx/PrFmzuPDCC+PaT93iMtWRiHwUuFVVz3Hf3wCgqv8WU+c5t87r7mzme4Fi4PrYurH1+vtOm+rIGDMYmzdvZtasWYluxqjS3t7O3Llzeeutt8jLy+u1Tm/9PtCpjpIhYWEZ8MYh25bFqV29GooRPMaY5Hb1wgzSGgKJbsaIl5HqYWJ+Bi+88AJXXHEF//Iv/9JncDpW8QpQx5KwcCDbOjs4JKOuMcaYxPjEJz7B+++/P6TfEa8ANZiEhbWxCQsHuC3gZNQF7gfnEt/RNjaRI3iMMYmxefNmphVnJ7oZZhASnrDQLf+iO8qvEidh4ZtxapcxxvQYqemFRqpj7e+EJyx06z0JbALCwNU2gs8YE28+n4+mpiZLuTFMuvNB+Xy+o96HJSw0xowJllF3+PWVUXe4R/EZY0xSS01NPSyzq0luNhefMcaYpGQByhhjTFKyAGWMMSYpWYAyxhiTlCxAGWOMSUoWoIwxxiQlC1DGGGOSkgUoY4wxSemYA5SIFIjI8yKy1V2O66Pe5W6drSJyeUz5y24m3XXuq+RY22SMMWbki8cZ1PXAi6o6A3jRfX8QESkAbsHJEfUR4JZDAtmlqrrAfdXHoU3GGGNGuHgEqGXAI+76I0BvuX/PAZ5X1f2qegB4Hjg3Dt9tjDFmlIpHgBqvqnUA7rK3S3S9ZdyNzZr7kHt571+ln2mGReQqEVkrImsbGhri0HRjjDHJakCTxYrIC8CEXj66aYDf01/W3EtVdbeI5ABPAZcBj/a2k3glLDTGGJP8BhSgVPUTfX0mIvtEpFRV60SkFOjtHlItsCTmfTnwsrvv3e7SLyKP49yj6jVAxaqurm4UkV0Daf9RKAIah2jfI4n1g/VBN+sHh/VDfPpgykAqHXM+KBG5C2hS1TtF5HqgQFV/cEidAqAaONEtegtYBLQC+araKCKpwBPAC6p63zE16hiJyNqB5CoZ7awfrA+6WT84rB+Gtw/icQ/qTuAsEdkKnOW+R0SqROQ3AKq6H7gdJzX8GuA2tywdeE5E1gPrgN3AA3FokzHGmBHumBMWqmoTsLSX8rXA12LerwBWHFKnDedMyhhjjDmIzSTRu/sT3YAkYf1gfdDN+sFh/TCMfXDM96CMMcaYoWBnUMYYY5LSmAhQIrJCROpFZENM2XwReV1E3hGRP4tIbsxn89zPNrqf+9zyO0TkAxEJJOI4jtVg+kFELo2ZH3GdiERFZIH72Vjqh1QRecQt3ywiN/S3n5FikH2QJiIPueVvi8iSmG1G+m9hkoi85P633Sgi17rlvc4xKo57RKRGRNaLyIkx+/qbiDSLyDOJOp6jcRR9cLz7O+kUkesO2Vd8/yZUddS/gNNxhrhviClbA5zhrl8B3O6ue4H1wHz3fSHgcddPBkqBQKKPaaj74ZDt5gLbY96PmX4ALgFWuuuZwE6goq/9jJTXIPvgauAhd70E55GRlFHyWygFTnTXc4AtwGzgp8D1bvn1wL+76+cBf8WZfOBkYHXMvpYCFwDPJPq4hrgPSoCTgDuA6470uzqW15g4g1LVV4D9hxQfB7zirj8PfNZdPxtYr6pvu9s2qWrEXX9D3WmdRqJB9kOsL+E8o9a9n7HUDwpkiYgXyAC6cJ7f62s/I8Ig+2A2zkTQqDOZczNQ5b4f6b+FOlV9y133A5txpmHra47RZcCj6ngDyHcnKEBVXwT8w9n+eBhsH6hqvaquAUK97CuufxNjIkD1YQPwaXf988Akd30moCLynIi8JSI/6HXr0aOvfoh1MTEBapTqqx9+D7QBdcD7wN3qPMM3GvXVB28Dy0TEKyKVOI+G9PY7GdFEpAJYCKym7zlGjzSv6Ig2wD4YNmM5QF0BXC0i1TintV1uuRc4DbjUXX5GRA57zmsU6asfABCRxUC7qo64+yyD1Fc/fASIABOBSuB7IjI1MU0ccn31wQqcf4jXAr8AXgPCCWnhEBGRbJy5QL+rqq39Ve2lbFQMhR5EHwybY35Qd6RS1XdxLuchIjOBT7kf1QL/UNVG97Nnca6pvpiIdg61fvqh2xcZ/WdP/fXDJcDfVDUE1IvI/+Bc3tqekIYOob76QFXDwP/uricirwFbE9HGoSDONGtPAY+p6h/c4r7mGK3l4LPHcmDP8LV2aAyyD4bNmD2DEjdzr4ikADcD3fP/PQfME5FM977DGcCmxLRy6PXTD91lnwdWJqZ1w6effngf+Lg7eisL58b4u4lp5dDqqw/cv4Usd/0sIKyqo+JvQkQEeBDYrKo/i/noaaA78/flwKqY8q+4v4eTgZaRfA8OjqoPhk+iR5AMxwvnDKAO56ZeLXAlcC3OaJUtOPMHSkz9LwMbca7J/zSm/Kfu9lF3eWuij22I+2EJ8EYv+xkz/QBkA79zfw+bgO/3t59EH9sQ9UEF8B7OzfMXgCmj6LdwGs4luu75QNfhjNQrxLlqstVdFrj1BbgX2Aa8A1TF7Ou/gQagw+2LcxJ9fEPUBxPc42vFGTBTC+T29bs6lrbZTBLGGGOS0pi9xGeMMSa5WYAyxhiTlCxAGWOMSUoWoIwxxiQlC1DGGGOSkgUoY4wxSckClDHGmKRkAcoYY0xSsgBljDEmKVmAMsYYk5QsQBljjElKFqCMMcYkJQtQxhhjklLcApSInCsi74lIjYhc38vn6SLyn+7nq93UwohIhYh0iMg693XfodsaY4wZe+KSUVdEPDg5Us7CyQGyRkSe1oOTml0JHFDV6SLyReDfgYvdz7ap6oJ4tMUYY8zoEK+U7x8BalR1O4CIrASWcXAm2mXAre7674H/52ZyPCpFRUVaUVFxtJsbY4xJkOrq6kZVLT5SvXgFqDLgg5j3tcDivuqoalhEWnAyNgJUisg/cTI03qyq/32kL6yoqGDt2rXH3HBjjDHDS0R2DaRevAJUb2dCh6bq7atOHTBZVZtEZBHwJxGZo6qth32JyFXAVQCTJ08+xiYbY4xJZvEaJFELTIp5Xw7s6auOiHiBPGC/qnaqahOAqlYD24CZvX2Jqt6vqlWqWlVcfMSzQ2OMMSNYvALUGmCGiFSKSBrwReDpQ+o8DVzurn8O+C9VVREpdgdZICJTgRnA9ji1yxhjzAgVl0t87j2lbwPPAR5ghapuFJHbgLWq+jTwIPBbEakB9uMEMYDTgdtEJAxEgG+o6v54tMsYYwYrFApRW1tLMBhMdFNGPJ/PR3l5OampqUe1vageeqtoZKiqqlIbJGGMibcdO3aQk5NDYWEhxzDQeMxTVZqamvD7/VRWVh70mYhUq2rVkfZhM0kYY0yMYDBowSkORITCwsJjOhO1AGWMMYew4BQfx9qPFqCMMcYkJQtQxhiTZDweDwsWLOCEE07gggsuoLm5+aj287WvfY1NmzYdVv7www/z7W9/+6jbl52dfdTbDoYFKGOMSTIZGRmsW7eODRs2UFBQwL333ntU+/nNb37D7Nmz49y64ROvmSSMMWbU+dGfN7Jpz2GT2hyT2RNzueWCOQOu/9GPfpT169f3vL/rrrt48skn6ezs5DOf+Qw/+tGPaGtr4wtf+AK1tbVEIhH+9V//lYsvvpglS5Zw9913U1VVxUMPPcS//du/UVpaysyZM0lPTwdg+fLlnH/++Xzuc58DnLOjQCBAIBBg2bJlHDhwgFAoxI9//GOWLVt2UNvq6uq4+OKLaW1tJRwO8+tf/5qPfexjceglhwUoY4xJUpFIhBdffJErr7wSgL///e9s3bqVN998E1Xl05/+NK+88goNDQ1MnDiRv/zlLwC0tLQctJ+6ujpuueUWqqurycvL48wzz2ThwoX9frfP5+OPf/wjubm5NDY2cvLJJ/PpT3/6oIEPjz/+OOeccw433XQTkUiE9vb2uB6/BShjjOnDYM504qmjo4MFCxawc+dOFi1axFlnnQU4Aervf/97T3AJBAJs3bqVj33sY1x33XX88Ic/5Pzzzz/sLGb16tUsWbKE7iniLr74YrZs2dJvG1SVG2+8kVdeeYWUlBR2797Nvn37mDBhQk+dk046iSuuuIJQKMSFF17IggXxzZpk96CMMSbJdN+D2rVrF11dXT33oFSVG264gXXr1rFu3Tpqamq48sormTlzJtXV1cydO5cbbriB22677bB99jXk2+v1Eo1Ge/bf1dUFwGOPPUZDQwPV1dWsW7eO8ePHH/ZM0+mnn84rr7xCWVkZl112GY8++mg8uyHxGXXdz25wy98TkXPi1SZjjBnJ8vLyuOeee7j77rsJhUKcc845rFixgkAgAMDu3bupr69nz549ZGZm8uUvf5nrrruOt95666D9LF68mJdffpmmpiZCoRC/+93vej6rqKiguroagFWrVhEKhQDnMmFJSQmpqam89NJL7Np1eIaMXbt2UVJSwte//nWuvPLKw773WCU8o66IzMaZl28OMBF4QURmqmokHm0zxpiRbOHChcyfP5+VK1dy2WWXsXnzZv5/e3cbY9VRx3H8+6OsLl3aILXYbWkKPlRoYkvXlbJJY4mIGA2uxjTWrkpS5RUvqrE0NGpKbBorGGNMDAYVgsHaqDVRGy0BItbYUnkIUGBrwUbrIgosVmg1ttC/L85sctjeu+zufTr37u+T3Nw5c+bMzvlzLpPzNNPT0wNkDzRs3ryZo0ePsnLlSiZNmkRbWxvr1q27oI7Ozk5Wr15NT08PnZ2ddHV1cf589l/s8uXL6e3tZf78+SxatIiOjg4A+vr6WIU64mIAAAcpSURBVLp0Kd3d3cybN485c+a8rm07duxg7dq1tLW1MXXq1KqfQVVlLD5JPcDqiFiSlu8DiIiv5cpsSWWeStNt/AO4EliVL5svN9LfrGQsvlo8mWNmrWHFzVO4ZvbbG92MwprSdglXT5sy6vL9/f3MnTv3grx6j8VXakbda8qViYhzwNCMuqPZFsgmLJS0W9LukydPVqnpZmZWREWYUXc022aZEeuB9ZCdQY2lgXmNejLHzIqvv7+ft11Zn5ESbGQNn1F3lNuamdVNs05DVDSVxrHhM+qm/DvSU36zyWbU/WOV2mVmNibt7e0MDg66k6rQ0HxQ7e3t466j4TPqpnI/AQ4D54AVfoLPzBpl5syZDAwM4PvclRuaUXe8PKOumZnVlWfUNTOzpuYOyszMCskdlJmZFZI7KDMzKyR3UGZmVkjuoMzMrJDcQZmZWSG5gzIzs0KquIOSNF3SVklH0vebypRblsockbQsl78jTVS4L31mVNomMzNrftU4g1oFbI+IdwDb0/IFJE0H7gduAeYD9w/ryPoiYl76nKhCm8zMrMlVo4PqBTal9CbgoyXKLAG2RsTpiPgXsBX4YBX+tpmZtahqdFBviYjjAOm71CW6i01KuDFd3vuKpFLzQwGesNDMbCIZ1WjmkrYBV5VY9aVR/p2RJiXsi4hjki4DHgU+DZSc2L5aExaamVnxjaqDioj3l1sn6Z+SOiPiuKROoNQ9pAFgYW55JrAj1X0sfZ+V9DDZPaqSHVTenj17Tkn662jaPw5vBk7VqO5m4jg4BkMch4zjUJ0YXDeaQhVPtyFpLTAYEQ9JWgVMj4h7h5WZDuwBulLWXuDdwBlgWkScktQG/BjYFhHfrahRFZK0ezRDwbc6x8ExGOI4ZByH+sagGvegHgIWSzoCLE7LSOqW9H2AiDgNPEA28+4u4Ksp743AFkkHgH3AMeB7VWiTmZk1uYpn1I2IQWBRifzdwOdyyxuADcPKvEx2JmVmZnYBjyRR2vpGN6AgHAfHYIjjkHEc6hiDpp3y3czMWpvPoMzMrJDcQZmZWSFNiA5K0gZJJyQdzOXdJOkpSc9I+pWky3PrbkzrDqX17Sn/QUl/k/RSI/ajUmOJg6S+3AC++yS9JmleWjeR4tAmaVPK75d030j1NIsxxuANkjam/P2SFua2afZj4VpJv03/tock3Z3ySw6Crcy3JR2VdEBSV66uxyW9KOmxRu3PeIwjBnPScfI/SfcMq6u6v4mIaPkP8F6yd7AO5vJ2Abel9F3AAyk9GTgA3JSWrwAuSekFQCfwUqP3qdZxGLbdu4Dnc8sTJg7AncAjKX0p8BdgVrl6muUzxhisADam9Ayydxontcix0Al0pfRlwHPADcAaYFXKXwV8PaU/BPyGbHScBcDTuboWAUuBxxq9XzWOwQzgPcCDwD0XO64q+UyIM6iIeAI4PSz7ncATKb0V+HhKfwA4EBH707aDEXE+pXdGGnewGY0xDnmfJHuJeqieiRSHADokTQamAK+QvWBerp6mMMYY3EA2UwGRzTbwItCdlpv9WDgeEXtT+izQTzZOaLlBsHuBH0ZmJzAtjaBDRGwHztaz/dUw1hhExImI2AW8WqKuqv4mJkQHVcZB4CMpfTtwbUpfD4SkLZL2Srq35Nato1wc8j5BroNqUeXi8DPgZeA48ALwjcheMm9F5WKwH+iVNFnSbLJ3F0sdJ01N0izgZuBpyg+CfbGBr5vaKGNQNxO5g7oLWCFpD9lp7SspfzJwK9CXvj8m6XUvIreQcnEAQNItwH8iounus4xRuTjMB84DVwOzgS9Kemtjmlhz5WKwgew/4t3At4AngXMNaWGNSJpKNlj15yPizEhFS+S1xLs6Y4hB3VQ8kkSziohnyS7nIel64MNp1QDwu4g4ldb9muya6vZGtLPWRojDkDto/bOnkeJwJ/B4RLwKnJD0B7LLW883pKE1VC4GEXEO+MJQOUlPAkca0cZaUDYO6KPAjyLi5ym73CDYA1x49jgT+Hv9WlsbY4xB3UzYMyilqeUlTQK+DAwNULsFuFHSpem+w23A4ca0svZGiMNQ3u3AI41pXf2MEIcXgPelp7c6yG6MP9uYVtZWuRik30JHSi8GzkVES/wmJAn4AdAfEd/MrfolsCyllwG/yOV/Jh0PC4B/N/M9OBhXDOqn0U+Q1ONDdgZwnOym3gDwWeBusqdVniMb4Fa58p8CDpFdk1+Ty1+Ttn8tfa9u9L7VOA4LgZ0l6pkwcQCmAj9Nx8NhYOVI9TR632oUg1nAn8hunm8DrmuhY+FWskt0QwNW7yN7Uu8KsqsmR9L39FRewHeAPwPPAN25un4PnAT+m2KxpNH7V6MYXJX27wzZAzMDwOXljqtK2uahjszMrJAm7CU+MzMrNndQZmZWSO6gzMyskNxBmZlZIbmDMjOzQnIHZWZmheQOyszMCun/xLO8a+luYdgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c19850dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decomposition = seasonal_decompose(usa)\n",
    "\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "plt.subplot(411)\n",
    "plt.plot(usa, label='Original')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal, label='Seasonality')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residuals')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have stationarized your time series, you could go on and model residuals (fit lines between values in the plot). However, as the patterns for the trend and seasonality information extracted from the series that are plotted after decomposition are still not consistent and cannot be scaled back to the original values, you cannot use this approach to create reliable forecasts.\n",
    "In case your time series does show a strong and consistent seasonal trend, here is a good article that describes the use of the SARIMAX model in Python.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove trend and seasonality with differencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistic                 -3.7101\n",
      "p-value                         0.0040\n",
      "#Lags Used                      0.0000\n",
      "Number of Observations Used    50.0000\n",
      "Critical Value (1%)            -3.5685\n",
      "Critical Value (5%)            -2.9214\n",
      "Critical Value (10%)           -2.5987\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1a555be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_diff = usa - usa.shift(periods=1)\n",
    "usa_diff.dropna(inplace=True)\n",
    "plt.plot(usa_diff)\n",
    "test_stationary(usa_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find optimal parameters and build an ARIMA (auto regressive integrated moving average) model\n",
    "\n",
    "To apply an ARIMA model to your time series, you need to find optimal values for the following three model parameters (p,d,q):\n",
    " - The number of autoregressive (AR) terms (p): AR terms are just lags of the dependent variable, the euro rate, in this case. So, if p=2, it means that predictors of x(t) will be x(t-1) and x(t-2).\n",
    " - The number of moving average (MA) terms (q): MA terms are lagged forecast errors in the prediction equation. For instance, if q=2, the predictors for x(t) will be e(t-1) and e(t-2) where e(i) is the difference between the moving average at i-th instant and the actual value.\n",
    " - The number of differences (d): These are the number of non-seasonal differences. In your case, d=1, as you are modeling using the first order differenced time series.\n",
    "\n",
    "There are two ways to determine the number of AR and MA terms. The first is to use the arma_order_select_ic function in Python. The second uses plots of the autocorrelation function (ACF) and partial autocorrelation function (PACF).\n",
    "[This article](http://people.duke.edu/~rnau/411arim3.htm) describes in detail the purpose of the ACF and PACF plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1a69ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ACF and PACF plots\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "def plot_acf_pacf(ts):\n",
    "    lag_acf = acf(ts, nlags=10)\n",
    "    lag_pacf = pacf(ts, nlags=10, method='ols')\n",
    "\n",
    "    #Plot ACF: \n",
    "    plt.subplot(121) \n",
    "    plt.plot(lag_acf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-7.96/np.sqrt(len(ts)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=7.96/np.sqrt(len(ts)),linestyle='--',color='gray')\n",
    "    plt.title('Autocorrelation Function')\n",
    "\n",
    "    #Plot PACF:\n",
    "    plt.subplot(122)\n",
    "    plt.plot(lag_pacf)\n",
    "    plt.axhline(y=0,linestyle='--',color='gray')\n",
    "    plt.axhline(y=-7.96/np.sqrt(len(ts)),linestyle='--',color='gray')\n",
    "    plt.axhline(y=7.96/np.sqrt(len(ts)),linestyle='--',color='gray')\n",
    "    plt.title('Partial Autocorrelation Function')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    \n",
    "usa_diff = usa - usa.shift(periods=1)\n",
    "usa_diff.dropna(inplace=True)\n",
    "plot_acf_pacf(usa_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this plot, the 'p' and 'q' values can be determined as follows:\n",
    " - p: The lag value where the PACF cuts off (drops to 0) for the first time. If you look closely, p=2.\n",
    " - q: The lag value where the ACF chart crosses the upper confidence interval for the first time. If you look closely, q=0.\n",
    "\n",
    "This means that the optimal values for the ARIMA(p,d,q) model are (2,1,0).\n",
    "\n",
    "If your assessment of the ACF and PACF plots differs from the values suggested by the arma_order_select_ic function, you should plug in different values for the p and q terms and use the model fit results to study the AIC values and proceed with the model with a lower AIC value\n",
    "\n",
    "Run the next code cell to plot the ARIMA model using the values (2,1,0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n",
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  elif issubdtype(paramsdtype, complex):\n",
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c1a598ba8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18ad0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ARIMA(usa, order=(2, 1, 0))  \n",
    "results_ARIMA = model.fit(disp=-1)  \n",
    "plt.plot(usa_diff, label = \"Original\")\n",
    "plt.plot(results_ARIMA.fittedvalues, color='red', label = \"fitted\")\n",
    "plt.title('RSS: %.4f'% sum((results_ARIMA.fittedvalues - usa_diff)**2))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measure the variance between the data and the values predicted by the model\n",
    "\n",
    "You can measure whether the results of your model fit the underlying data by using the residual sum of squares (RSS) metric. A small RSS indicates that the model fits tightly to the data.\n",
    "Yet another approach to validate the ARIMA model appropriateness is by performing residual analysis.\n",
    "Print the results of the ARIMA model and plot the residuals. A density plot of the residual error values indicates a normal distribution centered around zero mean. Also, the residuals do not violate the assumptions of constant location and scale with most values in the range (-1,1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:            D.PerCapGDP   No. Observations:                   51\n",
      "Model:                 ARIMA(2, 1, 0)   Log Likelihood                -393.388\n",
      "Method:                       css-mle   S.D. of innovations            539.434\n",
      "Date:                Thu, 10 May 2018   AIC                            794.777\n",
      "Time:                        15:21:26   BIC                            802.504\n",
      "Sample:                    01-01-1961   HQIC                           797.730\n",
      "                         - 01-01-2011                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const               910.5975    159.148      5.722      0.000     598.674    1222.521\n",
      "ar.L1.D.PerCapGDP     0.6227      0.139      4.481      0.000       0.350       0.895\n",
      "ar.L2.D.PerCapGDP    -0.0889      0.140     -0.634      0.529      -0.364       0.186\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                 Real           Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            2.4928           +0.0000j            2.4928            0.0000\n",
      "AR.2            4.5141           +0.0000j            4.5141            0.0000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n",
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  elif issubdtype(paramsdtype, complex):\n"
     ]
    }
   ],
   "source": [
    "print(results_ARIMA.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               0\n",
      "count    51.0000\n",
      "mean      6.7815\n",
      "std     549.1698\n",
      "min   -1891.6571\n",
      "25%    -259.9499\n",
      "50%     -62.5588\n",
      "75%     373.6328\n",
      "max    1849.7060\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1a6be128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = pd.DataFrame(results_ARIMA.resid)\n",
    "residuals.plot(kind='kde')\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model is returning the results you want to see, you can scale the model predictions back to the original scale. For this, you will remove the first order differencing and take exponent to restore the predictions back to their original scale.\n",
    "The lower the root mean square error (RMSE) and the closer it is to 0, the better are the model predictions in being closer to actual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1961-01-01    910.5975\n",
       "1962-01-01    423.8494\n",
       "1963-01-01    529.6116\n",
       "1964-01-01    490.1171\n",
       "1965-01-01    537.0711\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_ARIMA_diff = pd.Series(results_ARIMA.fittedvalues, copy=True)\n",
    "predictions_ARIMA_diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()\n",
    "predictions_ARIMA = pd.Series(usa.ix[0], index=usa.index)\n",
    "predictions_ARIMA = predictions_ARIMA.add(predictions_ARIMA_diff_cumsum, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c1a68d518>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1aa025c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(usa, label = \"Actual\")\n",
    "plt.plot(predictions_ARIMA, label = \"ARIMA fitted\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing Predicted vs Expected Values...\n",
      "\n",
      "\n",
      "predicted=48414.891491, expected=48061.537660\n",
      "predicted=49581.111548, expected=48401.427340\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n",
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  elif issubdtype(paramsdtype, complex):\n",
      "/Users/abulbasar/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  if issubdtype(paramsdtype, float):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=48751.221681, expected=47001.555350\n",
      "predicted=45550.703440, expected=48373.878820\n",
      "predicted=49764.522981, expected=49790.665480\n",
      "\n",
      "\n",
      "Printing Mean Squared Error of Predictions...\n",
      "Test MSE: 2509769.737619\n"
     ]
    }
   ],
   "source": [
    "size = int(len(usa) - 5)\n",
    "train, test = usa[0:size], usa[size:len(usa)]\n",
    "history = [x for x in train]\n",
    "predictions = list()\n",
    "\n",
    "print('Printing Predicted vs Expected Values...')\n",
    "print('\\n')\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(2,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0]\n",
    "    predictions.append(float(yhat))\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "    print('predicted=%f, expected=%f' % (yhat, obs))\n",
    "\n",
    "error = mean_squared_error(test, predictions)\n",
    "\n",
    "print('\\n')\n",
    "print('Printing Mean Squared Error of Predictions...')\n",
    "print('Test MSE: %.6f' % error)\n",
    "\n",
    "predictions_series = pd.Series(predictions, index = test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1b1d7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rolling one-step out-of-sample\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(title='GDP Per Capita forecasting', xlabel='Date', ylabel='Per Capita GDP')\n",
    "ax.plot(usa, marker = '.', color = \"red\", label='observed')\n",
    "ax.plot(predictions_series, color = \"green\", label='forecast')\n",
    "legend = ax.legend(loc='upper left')\n",
    "legend.get_frame().set_facecolor('w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
